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Update README.md

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  1. README.md +18 -14
README.md CHANGED
@@ -95,7 +95,7 @@ quantization_config = BitsAndBytesConfig(
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  bnb_4bit_use_double_quant=True,
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  )
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- model_id = "EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.005-128K-code-COT"
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  pipeline = transformers.pipeline(
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  "text-generation",
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  model=model_id,
@@ -104,12 +104,13 @@ pipeline = transformers.pipeline(
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  )
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  messages = [
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  {"role": "system", "content": """
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- You should reason about the input and provide a logical explanation.
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- The explanation should follow these rules:
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- - The explanation should be written at graduate level engineering, science, math and literature
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- - The explanation should be split into subtasks
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- - The explanation should always end with 2-3 related concepts.
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- - subtasks have their own chain of thoughts
 
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  """},
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  {"role": "user", "content": "Create a bar plot showing the market capitalization of the top 7 publicly listed companies using matplotlib"}
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  ]
@@ -141,7 +142,7 @@ fourbit_models = [
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  ] # More models at https://huggingface.co/unsloth
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  model, tokenizer = FastLanguageModel.from_pretrained(
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- model_name = "EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.005-128K-code-COT",
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  max_seq_length = 128000,
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  load_in_4bit = True,
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  token =userdata.get('HF_TOKEN')
@@ -150,12 +151,14 @@ def chatbot(query):
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  messages = [
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  {"from": "system", "value":
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  """
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- You should reason about the input and provide a logical explanation.
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- The explanation should follow these rules:
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- - The explanation should be written at graduate level engineering, science, math and literature
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- - The explanation should be split into subtasks
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- - The explanation should always end with 2-3 related concepts.
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- - subtasks have their own chain of thoughts
 
 
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  """
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  },
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  {"from": "human", "value": query},
@@ -166,6 +169,7 @@ The explanation should follow these rules:
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  _ = model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 2048, use_cache = True)
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  ```
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  # Response
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  ```python
 
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  bnb_4bit_use_double_quant=True,
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  )
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+ model_id = EpistemeAI2/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.005-128K-code-COT"
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  pipeline = transformers.pipeline(
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  "text-generation",
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  model=model_id,
 
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  )
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  messages = [
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  {"role": "system", "content": """
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+ Environment: ipython. Tools: brave_search, wolfram_alpha. Cutting Knowledge Date: December 2023. Today Date: 4 October 2024\n
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+ You are a coding assistant with expert with everything\n
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+ Ensure any code you provide can be executed \n
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+ with all required imports and variables defined. List the imports. Structure your answer with a description of the code solution. \n
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+ write only the code. do not print anything else.\n
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+ debug code if error occurs. \n
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+ Here is the user question: {question}
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  """},
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  {"role": "user", "content": "Create a bar plot showing the market capitalization of the top 7 publicly listed companies using matplotlib"}
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  ]
 
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  ] # More models at https://huggingface.co/unsloth
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  model, tokenizer = FastLanguageModel.from_pretrained(
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+ model_name = "EpistemeAI2/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.005-128K-code-COT",
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  max_seq_length = 128000,
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  load_in_4bit = True,
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  token =userdata.get('HF_TOKEN')
 
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  messages = [
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  {"from": "system", "value":
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  """
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+ Environment: ipython. Tools: brave_search, wolfram_alpha. Cutting Knowledge Date: December 2023. Today Date: 4 October 2024\n
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+ You are a coding assistant with expert with everything\n
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+ Ensure any code you provide can be executed \n
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+ with all required imports and variables defined. List the imports. Structure your answer with a description of the code solution. \n
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+ write only the code. do not print anything else.\n
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+ use ipython for search tool. \n
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+ debug code if error occurs. \n
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+ Here is the user question: {question}
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  """
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  },
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  {"from": "human", "value": query},
 
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  _ = model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 2048, use_cache = True)
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  ```
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+
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  # Response
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  ```python